"Reconstruction of long-term data sets of land surface temperature (LST) based on MODIS" developed by  Professor Mao Kebiao of the Innovation Team of Grassland Ecology and Remote Sensing of the Institute of Agricultural Resources and Regional Planning (IARRP), CAAS was selected as one of the "Top Ten Most Valuable Annual Data Sets", and "Most Contributing Data Team" titles.

LST is a key variable in studies such as high temperature and drought monitoring, as well as climate change. At present, thermal infrared remote sensing technology has become an important means to quickly obtain ground temperature in a large range. However, the surface temperature obtained by thermal infrared remote sensing is easily affected by clouds, and the lack of values in the image caused by cloud  is an important factor restricting the application  of thermal infrared surface temperature data.

The team developed a monthly-scale surface temperature product in China by establishing a reconstruction model that fuses site observation data and cloud-free thermal infrared surface temperature data. The validation indicates  that the product showed high performance in different regions of China.

In addition, based on the data product, the team conducted spatiotemporal change analysis of surface temperature in China over the past 15 years. The results show that the overall surface temperature change in China has a slight warming trend, but it is significantly uneven in different regions.

The research results have more advantages in improving the accuracy of regional surface temperature under cloudy conditions, and provide accurate and reliable surface temperature information for research on crop evapotranspiration and growth monitoring, water cycles, and climate change.

Paper download link: https://doi.org/10.5194/essd-12-2555-2020

Data download link: https://doi.org/10.5281/zenodo.3528024

A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017，Earth Syst. Sci. Data, 12, 2555–2577, 2020. https://doi.org/10.5194/essd-12-2555-2020 . （Impact factor 11.333）